OpenCV Python resize image
Date : March 29 2020, 07:55 AM
With these it helps The problem was I was trying to get coordinate picture that were negative so it couldn't get it. I just expanded condition if pt1+leng > width or pt2+leng > height or pt2 < 0 or pt1 < 0: and it works.
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Resize image faster in OpenCV Python
Tag : python , By : Cube_Zombie
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , If you are absolutely intent on doing this in Python, then please just disregard my answer. If you are interested in getting the job done simply and fast, read on... I would suggest GNU Parallel if you have lots of things to be done in parallel and even more so as CPUs become "fatter" with more cores rather than "taller" with higher clock rates (GHz). magick mogrify -resize 128x128\! *.jpg
parallel magick mogrify -resize 128x128\! ::: *.jpg
find -iname \*.jpg -print0 | parallel -0 -X --eta magick mogrify -resize 128x128\!
DIR /S /B *.JPG > filenames.txt
magick mogrify -resize 128x128\! @filenames.txt
parallel --eta -a filenames.txt magick mogrify -resize 128x128\!
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resize transparent image in opencv python (cv2)
Date : March 29 2020, 07:55 AM
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How to resize PNG image in opencv python?
Tag : python , By : Jakub Filak
Date : March 29 2020, 07:55 AM
I hope this helps . i am trying to resize the following PNG image without losing transparent background (alpha channel) using cv2.resize() function but it only shows the original image with same dimensions [![][1]][1] , Try something like this import cv2
img = cv2.imread('/home/img/python.png', cv2.IMREAD_UNCHANGED)
print('Original Dimensions : ',img.shape)
width = 350
height = 450
dim = (width, height)
# resize image
resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)
print('Resized Dimensions : ',resized.shape)
cv2.imshow("Resized image", resized)
cv2.waitKey(0)
cv2.destroyAllWindows()
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Image resize do not display the correct image in opencv python
Date : October 04 2020, 07:00 PM
Hope this helps By downsizing to 1 pixel you loose nearly all image information as all y-pixels get interpolated to a single number per x-pixel. By resizing back that pixel is then copied vertically to 500px, so I expect you to get a stripy pattern. You should not resize, you have to reshape. That means putting the pixel values from a 2d array to a 1d array, that is what the PCA algorithm expects. # create 2d array
y = np.array(range(9)).reshape(3,3)
print(y)
# reshape to 1d
x = y.reshape(-1)
print(x)
print(x.shape[:2])
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